Machine learning visualization

ABSTRACT

A unique user interface for improving machine learning algorithms is described herein. The user interface comprises an icon with multiple visual indicators displaying the machine learning confidence score. When a mouse hovers over the icon, a set of icons are displayed to accept the teaching user&#39;s input. In addition, the words that drove the machine learning confidence score are highlighted with formatting so that the teaching user can understand what drove the machine learning confidence score.

BACKGROUND

Prior Application

This application is a priority application. It is related to two designpatent applications, US Design Patent Application 29/678,877, “A LightBulb Indicator of a Machine Learning Confidence Score”, filed on Jan.31, 2019 and US Design Patent Application 29/678,886, “A User Interfacefor Collecting Machine Learning Feedback”, filed on Jan. 31, 2019. BothApplications are hereby incorporated by reference.

TECHNICAL FIELD

The system, apparatuses and methods described herein generally relate tomachine learning visualization, and, in particular, to visual techniquesfor displaying a machine learning confidence score and reasoning on adisplay screen.

DESCRIPTION OF THE RELATED ART

The name machine learning was coined in 1959 by Arthur Samuel. Tom M.Mitchell provided a widely quoted, more formal definition of thealgorithms studied in the machine learning field: “A computer program issaid to learn from experience E with respect to some class of tasks Tand performance measure P if its performance at tasks in T, as measuredby P, improves with experience E.” This definition of the tasks in whichmachine learning is concerned offers a fundamentally operationaldefinition rather than defining the field in cognitive terms.

Machine learning tasks are classified into several broad categories. Insupervised learning, the algorithm builds a mathematical model of a setof data that contains both the inputs and the desired outputs. Forexample, if the task were determining whether an image contained acertain object, the training data for a supervised learning algorithmwould include images with and without that object (the input), and eachimage would have a label (the output) designating whether it containedthe object. In special cases, the input may be only partially available,or restricted to special feedback. Semi-supervised learning algorithmsdevelop mathematical models from incomplete training data, where aportion of the sample inputs are missing the desired output.

Machine learning implementations have two different modes, one for theoperation of the algorithms and another for the leaning aspect. In asupervised machine learning implementation, both the learning fromtraining data and the actual supervised operation require that the databe specified as true or false as to the specific criteria. For instance,in the field of legal spend management, supervised machine learning isused to review legal invoices to see if the item billed fits within aset of billing criteria. If the invoice item fits within the criteria,the item is allowed. Otherwise, the invoice item is rejected. A machinelearning algorithm will parse the invoice item and match certain wordsagainst a model developed by the machine learning engine. The invoice isthen displayed to a user to validate the machines determination.

While some machine learning, Amazon storefront for example, incorporatesfeedback into its user interface by taking what you buy or search forand outputting similar products as suggestions, other machine learningtasks incorporate a simple agree/disagree user interface. But neitherinterface provides an explanation of why the machine made itsdetermination. There is a strong need in the industry to improve thecommunications from the machine to the supervising user as to why adetermination is made, and to provide an easy, intuitive means for thesupervising user to provide feedback to the machine.

The present inventions address these issues.

BRIEF SUMMARY OF THE INVENTION

A special purpose computer implemented method of visualizing a machinelearning confidence score is described herein. The method is made up ofthe steps of processing a textual description through a machine learningmodel to derive the machine learning confidence score. The machinelearning model uses natural language processing to convert the textualdescription into word stems that are used by the machine learning modelto calculate the machine learning confidence score. Once the confidencescore is determined, the algorithm then searches the textual descriptionfor word stems that comprised a highest impact in the confidence score.Formatting instructions are added in the textual description to at leastone word associated with the word stems that comprised the highestimpact on the confidence score. The textual description with theformatting instructions to indicate reasoning used by the machinelearning model in the determination of the machine learning confidencescore are then displayed on a display screen, and input from a usercorrecting the machine learning confidence score is accepted, the inputused to teach the machine learning model.

The method may use the textual description from a legal invoice, amedical invoice, or another document. A variable icon could be displayedon the display screen to graphically indicate the machine learningconfidence score. The variable icon could display a different number ofitems depending upon the magnitude of the confidence score; in someembodiments the variable icon displays three rays off of a lightbulbbased on one range of the confidence score, and one ray off of thelightbulb based on another range of the confidence score. The variableicon could change once the input from the user is accepted. The userinput could modify the machine learning model in real time.

An apparatus for the visualization of a machine learning confidencescore is also described herein. The apparatus is made up of a displayscreen, a special purpose computer electrically connected to the displayscreen, and a large capacity data storage facility with a machinelearning training data set. The apparatus further includes a userinterface display module operating on the special purpose computer anddisplaying an indication of the confidence score on the display screen;a natural language processing module operating on the special purposecomputer and interfacing with the user interface display module toconvert a textual description into a table of word stems; a machinelearning module operating on the special purpose computer andinterfacing with the natural language processing module to convert thetable of word stems into a confidence score using a machine learningmodel built using the machine learning training data set; a scan backmodule operating on the special purpose computer and interfacing to themachine learning module to insert formatting instructions into thetextual description using the table of stems to format at least one wordin the textual description that impacted the confidence score; and auser interface input module operating on the special purpose computerand interfacing with the scan back module to display the textualdescription with the formatting instructions and to accept user inputregarding the confidence score and to add the user input to the machinelearning training data set.

The apparatus may use the textual description from a legal invoice, amedical invoice, or another document. The indication of the confidencescore on the display screen could be in the form of a variable icon thatvaries depending on the magnitude of the confidence score. The variableicon could display a different number of items depending upon themagnitude of the confidence score; in some embodiments the variable icondisplays three rays off of a lightbulb based on one range of theconfidence score, and one ray off of the lightbulb based on anotherrange of the confidence score. The user input could modify the machinelearning model in real time.

A system for the visualization of a machine learning confidence score isalso described herein. The system is made up of a display screen on apersonal computing device and a special purpose computer electricallyconnected to the personal computing device through a network and to alarge capacity data storage facility with a machine learning trainingdata set. The system also includes a user interface display moduleoperating on the personal computing device and displaying the confidencescore on the display screen in the form of a variable icon that variesdepending on the magnitude of the confidence score; and a naturallanguage processing module operating on the special purpose computer toconvert a textual description into a table of word stems. In addition,the system includes a machine learning module operating on the specialpurpose computer and interfacing with the natural language processingmodule to convert the table of word stems into a confidence score usinga machine learning model built using the machine learning training dataset; a scan back module operating on the special purpose computer andinterfacing to the machine learning module to insert formattinginstructions into the textual description using the confidence score andthe table of stems to format at least one word in the textualdescription that impacted the confidence score; and a user interfaceinput module operating on the personal computing device and interfacingwith the scan back module through the network to display the textualdescription with the formatting instructions and to accept user inputregarding the confidence score and sending the user input to the specialpurpose computer to add the user input to the machine learning trainingdata set.

The system may use the textual description from a legal invoice or otherdocument. The variable icon could display a different number of itemsdepending upon the magnitude of the confidence score; in someembodiments the variable icon displays three rays off of a lightbulbbased on one range of the confidence score, and one ray off of thelightbulb based on another range of the confidence score. The variableicon could change once the input from the user is accepted. The userinput could modify the machine learning model in real time.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a screen shot of a legal invoice with the confidence scoreindication.

FIG. 2 is a screen shot of a legal invoice with a mouse hovering overthe confidence score indication.

FIG. 3 is a screen shot of a legal invoice with a mouse hovering overthe confidence score after the user has approved the machine score.

FIG. 4 illustrates a flow chart of the user interface code in oneembodiment.

FIG. 5 is a flow chart of machine learning model execution.

FIG. 6 is an electrical architecture of one embodiment.

DETAILED DESCRIPTION

There is a strong need in the machine learning industry to providemeaningful information to the supervising user in a way that isintuitive and easy to use. Since the teaching of the machine may includethousands of evaluations from users who have little extra time to trainmachine models, the easy of rapidly understanding the machine's rationaland inputting feedback are critical.

In the field of legal spend management, auditors are hired to reviewevery legal bill sent to an insurance company or other entity to assurethat the billing adheres to contractual billing guidelines. This task ofreviewing invoices requires trained auditors, often lawyers themselves,performing a tedious task of reading each line of each legal bill forcompliance. The reviewers vary in their interpretation due to thepersonalities of each auditor. In addition, the labor costs of reviewinglegal bills is high.

As a result, the legal spend management industry has started to use rulebased heuristics and machine learning analysis of legal invoices tolower costs and provide a consistent interpretation. While machinelearning provides an improvement over human review and heuristicdeterminations, in the analysis of the highly variable text in theinvoices, supervised machine learning is far from easy to use.

The present inventions provide a user interface that simply presents themachine's determination, both in terms of acceptance or rejection of aninvoice item, but also provides an indication of the machine'sconfidence in the determination.

While the example below uses legal invoices, the present inventionscould be applied to medical bills, fleet maintenance bills, or any othertype of invoices. Furthermore, any time of supervised machine learningcould benefit from the user interface and algorithms described herein.The narrative description 102 could explain the tasks of a lawyer, acourt reporter, an independent adjustor, an accountant, and engineer, anexpert witness, or any number of other worker.

Looking to FIG. 1, we see a portion of a legal invoice that has beenprocessed by a machine learning model. This portion of the invoice is atable of seven rows, where each row is an individual time card entry.Each entry has a date 101, a narrative 102, an amount 104, and othercolumns. There are a number of types of analysis that can be performedthat are not described herein, for instance assuring that the dates 101are within a range or that the calculations of the amount 104 arecorrect.

For purposes of our description in this embodiment, we focus on thenarrative 102, but the other columns, or other information on theinvoice or even beyond the invoice, may be used without detracting fromthe inventions herein. The narrative 102 is analyzed to determine if itfits within a criteria. In the first row, the machine believes that thetime cards entry in this row should be rejected, but the machine has alow confidence in its determination. This is seen because the light bulbindicator 103 has only one ray lit, indicating a low confidence. Themachine's confidence score for this row is determined by the model, asdescribed below. The machine, in its creation of the user interface, hasthe confidence score within a certain range that indicates a lowerconfidence.

The second row has no light bulb indicator, indicating that the machinebelieves that this time card entry is allowable. Technically, this meansthat the machine learning confidence score is below a threshold. Thethird row has a light bulb indicator 105 with all rays lit, indicatinghigh confidence that this time card entry should be rejected. The sixthrow has a light bulb indicator 107 with two rays lit, indicating mediumconfidence in the scoring. In one embodiment, the confidence score couldhave a range from 0 to 100. All confidence scores less than 40 could bedetermined to be billable, scores from 41-60 could display one ray 103,all confidence scores from 61-80 could display two rays 107, and allscores above 81 could display three rays 105. (The number of rays couldbe changed without detracting from the inventions herein).

In the fourth row, a partially disallowed time card entry is shown, with$175.00 deducted from this line item 106.

FIG. 2 shows the display screen when the mouse hovers over the lightbulb icon 205. The mouse over indicates that the user, who is teachingthe machine, is focusing on the particular line on of the invoice. Otherembodiments could include tracking the user's eyes to determine focus orwatching where a finger is hovering on a touch screen. In still anotherembodiment, every line of the invoice displays as in the third sectionof FIG. 2.

In this example, the mouse hovers over the light bulb icon 205 and theuser interface software detects the mouse over. The software thenpresents the user with three options to select: a thumbs up icon 202, ora thumbs down icon 203, or a comment icon 204.

If the user selects the thumbs up icon 202, the user is approving of themachine learning decision. The time card entry in the legal invoice forthat line is marked as disallowed, and the machine learning algorithm isaffirmed. The thumbs up icon 202 may be filled in (or a symbol (dot, forexample) inserted into it) to provide feedback to the user that hisinput has been received. In some embodiments, a separate user interfaceis provided to allow the user to allow a portion of this time cardentry.

If the user selects the thumbs down icon 203, the user is disapprovingof the machine learning decision. The time card entry in the legalinvoice for that line is marked as allowed, and the machine learningalgorithm is notified that the answer is incorrect. The machine learningalgorithm can adjust on the fly, or the correction can be batched up forthe next run of the model. The thumbs down icon 203 may be filled in (ora symbol (dot, for example) inserted into it) to provide feedback to theuser that his input has been received. This is shown in FIG. 3 as item302.

If the user selects the comment icon 204, the user is allowed to entercomments on the invoice line. The comment icon 204 is filled in (or hasa symbol (dot, for example) inserted into it) to indicate that there isa comment on this item. A second frame or window is presented to theuser to collect the comment.

Once the user has made a selection, a dot is placed in the middle of thelight bulb icon 202, as seen in item 304 of FIG. 3. Each time the mousehovers over the light bulb icon 304 after the user has made a selection,the thumbs up icon 202, thumbs down icon 203, or comment icon 204 willindicate the previous selection by filling in the selected choice (thethumbs down icon 302, for example).

The user has the option, once a selection is made, to select the icon202, 203, 204 again to remove the selection. In this case, the dot inthe lightbulb is removed, in one embodiment. The user may also selectthe other thumb icon 202, 203 to change the selection.

During the mouse over, the reasoning for the machine learning confidencescore is indicated in the narrative 201 column for that line of theinvoice 206 by changing the formatting of the words in the narrative. Inthe present embodiment, the words that drove the confidence score arebolded 207, 208 and the words that are the most important drivers of theconfidence score increase their font sizes 207. This allows the user toeasily see the machine's reasoning behind the confidence score. This issimilar to a teacher requiring a student to show their work as to whythey made the choice that they made.

In an alternative embodiment, the words that drive the confidence scorecould be displayed in a different color, italicized, single and doubleunderlined, or any other user interface indication.

In FIG. 3, we see the display screen with the user deciding todisapprove of the machine's determination of line three of the invoice.The thumbs down icon 302 is filled in and a dot is placed in the middleof the light bulb 304 to indicate that the user has made a selection forthis item. While the thumb icons 301, 302 and the comment icon 304 willdisappear when the mouse is no longer hovering over the light bulb 304,the light bulb icon 304 will continue to display the dot in the middleto indicate that the user has graded the machine's determination of thisline of the invoice. In other embodiments, the dot in the light bulb 304could be replaced with another type of indication, such as icon color orother visual change in the icon 304.

As in FIG. 2, the narrative line 306 includes a word cloud typeindication of the words that drove the machine learning confidencescore. In addition, the user may note 305 a partial or full dismissal ofthe invoice amount for that line item as well as a categorization of thereason for cutting the bill.

FIG. 4 shows one possible user interface algorithm for implementing theabove described user interface. The algorithm begins when the user (oran automaton) selects an invoice to review 401. The invoice is thenprocessed by the machine learning model 402. Each line of the invoice isanalyzed to determine a machine learning confidence score for the line.Based on the confidence score, different icons are displayed on thescreen to provide the user with information on the machine'sdetermination 403. In one embodiment, the confidence score could have arange from 0 to 100. All confidence scores less than 40 could bedetermined to be billable, with no icon displayed, scores from 41-60could display one ray 103, all confidence scores from 61-80 coulddisplay two rays 107, and all scores above 81 could display three rays105. (The number of rays and categories could be changed withoutdetracting from the inventions herein).

The mouse location is then checked by the user interface 404, and if themouse is over a light bulb icon 103, 105, 107, then the line isprocessed to indicate details of the reasoning. The word cloud of thenarrative text is determined 405 (see FIG. 5) and displayed 406,replacing the text of the narrative with the narrative text withformatting indicating the reasoning for the machine learning score. Thethumbs up icon 202, thumbs down icon 203, and comment icon 204 are alsodisplayed where the mouse is located.

If the mouse moves to another area of the screen, the formatting of thetext narrative and thumbs icons 202, 203, 204 will be removed from thescreen.

If the user selects one of the thumbs icons 202, 203, 204, the screenwill be updated to reflect the choice 408 The selected icon will befilled in (thumbs down 302, for example) and a dot will be placed in thelight bulb icon 304. The user's teaching indication will then be sentback to the machine learning model 409. The invoice is then revised, ifboth the machine and the user agree that the billing entry needs to berejected 410.

In some embodiments, when the invoice is run through the machinelearning model 402, a data structure is returned for each line in theinvoice with the narrative formatted to indicate the importance of eachword in the narrative to the determination of the confidence score. Inthis scenario, the functionality of 405 is incorporated in 402, andblock 406 simply replaces the original narrative with the storednarrative for that time card entry description.

FIG. 5 describes one embodiment the operation of the machine learningmodel for a line of the invoice 501. First, the test of the narrative isparsed into a table of words 502. This could use traditional parsingtechniques of searching for delimiters such as spaces, periods, tabs,commas, and other punctuation types. The strings between the delimitersare stored in a table. In some descriptions, this is called tokenization

The words in the table are next analyzed to convert the word into itsstem by removing parts of speech that are attached to the word, such asplurality 503. Some descriptions call finding this processlemmatization—figuring out the most basic form or lemma of each word inthe sentence. For some models, the word is categorized as a noun, verb,adverb, adjective, etc. Steps 502, 503, 504 are often referred to asnatural language processing.

Next, each word stem is looked up in the model's database of stems 505,and the corresponding weight of the word in the database is copied intothe narrative's table of words. In a simple model, the weights areaveraged to create a confidence score 506. More complicated models coulduse other statistical methods to determine the confidence score. Forinstance, verbs could be given more weight than adverbs, or a mean couldbe used rather than an average.

Once the confidence score has been determined, the algorithm modelbacktracks to format the narrative text according to the impact eachword has on the confidence score 507. This is done by looking at thetable to see the relative weight of each word. The highest weightedwords are identified, and the original text of the narrative is searchedfor each of these highest weighted words. When the word is found, it isconverted to bold format, and the font increased by a factor.

The confidence score and the formatted narrative string are thenreturned 508.

Because of the complexities of machine learning algorithms, specialpurpose computing may be needed to build and execute the machinelearning model described herein. FIG. 6 shows one such embodiment. Theuser views the user interface described here on a personal computingdevice such as a personal computer, laptop, tablet, smart phone,monitor, or similar device 601. The personal computing device 601communicated through a network 602 such as the Internet, a local areanetwork, or perhaps through a direct interface to the server 603. Theserver 603 is a high performance, multi-core computing device withsignificant storage facilities 604 in order to store the training datafor the model. Since this training data is continuously updated throughthe present inventions, this data must be kept online and accessible sothat it can be updated. In addition, the real-time editing of the modelas the user provides feedback to the machine 409 requires significantprocessing power to rebuild the model as feedback is received.

The server 603 is a high performance computing machine electricallyconnected to the network 602 and to the storage facilities 604.

While the above described embodiment involves machine learningconfidence scores, the algorithm could be used with confidence scoresusing other software techniques. For instance, it is envisioned that aheuristically based algorithm could also be used to analyze thenarrative field, and that the word cloud could be formed by insertingformatting into the narrative field at each step in the heuristicalalgorithm.

The foregoing devices and operations, including their implementation,will be familiar to, and understood by, those having ordinary skill inthe art. This algorithm is necessarily rooted in computer technology inorder to overcome the problem of displaying machine learning reasoningwith a simple interface in order to receive user feedback in themachine's determination of a confidence score.

The above description of the embodiments, alternative embodiments, andspecific examples, are given by way of illustration and should not beviewed as limiting. Further, many changes and modifications within thescope of the present embodiments may be made without departing from thespirit thereof, and the present invention includes such changes andmodifications.

The invention claimed is:
 1. A special purpose computer implementedmethod of visualizing a machine learning confidence score, the methodcomprising: processing a textual description through a machine learningmodel to derive the machine learning confidence score, wherein themachine learning model uses natural language processing to convert thetextual description into word stems that are used by the machinelearning model to calculate the machine learning confidence score, andthen searches the textual description for the word stems that compriseda highest impact in the machine learning confidence score, addingformatting instructions to bold or increase a font size at least oneword associated with the word stems that comprised the highest impact onthe machine learning confidence score; displaying on a display screenthe textual description with the formatting instructions to indicatereasoning used by the machine learning model in a determination of themachine learning confidence score, wherein the textual description withthe formatting instructions is only displayed on the display screen whena mouse location is over a variable icon that varies depending on amagnitude of the machine learning confidence score; and accepting inputfrom a user correcting the machine learning confidence score, said inputused to teach the machine learning model.
 2. The method of claim 1wherein the textual description is from a legal invoice.
 3. The methodof claim 1 wherein the textual description is from an expert witnessinvoice.
 4. The method of claim 1 further comprising displaying thevariable icon on the display screen to graphically indicate the machinelearning confidence score.
 5. The method of claim 4 wherein the variableicon displays a different number of items depending upon the magnitudeof the machine learning confidence score.
 6. The method of claim 5wherein the variable icon displays three rays off of a lightbulb basedon one range of the machine learning confidence score, and one ray offof the lightbulb based on another range of the machine learningconfidence score.
 7. The method of claim 4 wherein the variable iconchanges once the input from the user is accepted.
 8. The method of claim1 wherein the user input modifies the machine learning model in realtime.
 9. An apparatus for the visualization of a machine learningconfidence score, the apparatus comprising: a display screen; a specialpurpose computer electrically connected to the display screen; a largecapacity data storage facility with a machine learning training dataset; a user interface display module operating on the special purposecomputer and displaying an indication of the machine learning confidencescore on the display screen; a natural language processing moduleoperating on the special purpose computer and interfacing with the userinterface display module to convert a textual description into a tableof word stems; a machine learning module operating on the specialpurpose computer and interfacing with the natural language processingmodule to convert the table of the word stems into the machine learningconfidence score using a machine learning model built using the machinelearning training data set; a scan back module operating on the specialpurpose computer and interfacing to the machine learning module toinsert formatting instructions to bold or increase a font size into thetextual description using the table of stems to format at least one wordin the textual description that impacted the machine learning confidencescore; and a user interface input module operating on the specialpurpose computer and interfacing with the scan back module to displaythe textual description with the formatting instructions and to acceptuser input regarding the machine learning confidence score and to addthe user input to the machine learning training data set, wherein thetextual description with the formatting instructions is only displayedon the display screen when a mouse location is over a variable icon thatvaries depending on a magnitude of the machine learning confidencescore.
 10. The apparatus of claim 9 wherein the textual description isfrom a legal invoice.
 11. The apparatus of claim 9 wherein the textualdescription is from an independent adjustor invoice.
 12. The apparatusof claim 9 wherein the indication of the machine learning confidencescore on the display screen is in a form of the variable icon thatgraphically varies depending on the magnitude of the machine learningconfidence score.
 13. The apparatus of claim 12 wherein the variableicon displays a different number of items depending upon the magnitudeof the machine learning confidence score.
 14. The apparatus of claim 13wherein the variable icon displays three rays off of a lightbulb basedon one range of the machine learning confidence score, and one ray offof the lightbulb based on another range of the machine learningconfidence score.
 15. The apparatus of claim 9 wherein the user inputmodifies the machine learning model in real time.
 16. A system for avisualization of a machine learning confidence score, the systemcomprising: a display screen on a personal computing device; a specialpurpose computer electrically connected to the personal computing devicethrough a network; a large capacity data storage facility electricallyconnected to the special purpose computer, the large capacity datastorage facility containing a machine learning training data set; a userinterface display module operating on the personal computing device anddisplaying the machine learning confidence score on the display screenin a form of a variable icon that varies depending on a magnitude of themachine learning confidence score; a natural language processing moduleoperating on the special purpose computer to convert a textualdescription into a table of word stems; a machine learning moduleoperating on the special purpose computer and interfacing with thenatural language processing module to convert the table of the wordstems into the machine learning confidence score using a machinelearning model built using the machine learning training data set; ascan back module operating on the special purpose computer andinterfacing to the machine learning module to insert formattinginstructions to bold or increase a font size into the textualdescription using the machine learning confidence score and the table ofword stems to format at least one word in the textual description thatimpacted the machine learning confidence score; and a user interfaceinput module operating on the personal computing device and interfacingwith the scan back module through the network to display the textualdescription with the formatting instructions and to accept user inputregarding the machine learning confidence score and sending the userinput to the special purpose computer to add the user input to themachine learning training data set, wherein the textual description withthe formatting instructions is only displayed on the display screen whena mouse location is over the variable icon.
 17. The system of claim 16wherein the textual description is from a legal invoice.
 18. The systemof claim 16 wherein the variable icon displays a different number ofitems depending upon the magnitude of the machine learning confidencescore.
 19. The system of claim 18 wherein the variable icon displaysthree rays off of a lightbulb based on one range of the machine learningconfidence score, and one ray off of the lightbulb based on anotherrange of the machine learning confidence score.
 20. The system of claim16 wherein the variable icon changes once the user input from the useris accepted.